The invention relates to the field of machine learning, and in particular to transfer learning from a source domain to train a classifier for a target domain.
In the following, a number of publications will be discussed. This discussion is not a concession that it would be obvious to combine concepts from these publications.
Typically there are plenty of labeled examples in the source domain, whereas very few or no labeled examples in the target domain. Transfer learning is useful in many real applications. One example is sentiment analysis. Sentiment analysis may appear in the case of movie reviews. Movie reviews may be labeled, for instance by having received ratings from viewers (labels obtained according to the movie ratings). From such existing reviews, an attempt is made to compare or predict polarity of reviews about some other product, such as an electronic product. An article dealing with this type of problem is J. Blitzer, M. Dredze, and F. Pereira, “Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classication,” In ACL, 2007 (“Blitzer et al.”). Another example might be in face recognition. In this area, there are many training images under certain lightening and occlusion conditions based on which a model is trained, but practically the model will be used under totally different conditions. An example of this area of application can be found in A. M. Martinez, “Recognition of partially occluded and/or imprecisely localized faces using a probabilistic approach,” CVPR, pages 1712-1717, 2000
Transfer learning can fall into various scenarios, such as:                1. The source domain and the target domain have the same feature space and the same feature distribution, and only the labeling functions are different, such as multi-label text classification J. Zhang, Z. Ghahramani, and Y. Yang, “Learning multiple related tasks using latent independent component analysis.” In NIPS, 2005;        2. The source domain and the target domain have the same feature space, but the feature distribution and the labeling functions are different, such as sentiment classification for different purposes, Blitzer et al., which sometimes is formalized as the problem that the training set and the test set have different feature distribution, W. Dai, Q. Yang, G.-R. Xue, and Y. Yu. Boosting for transfer learning. In ICML, pages 193-200, 2007.        3. The source domain and the target domain have different feature space, feature distribution and labeling functions, such as verb argument classification, S.-I. Lee, V. Chatalbashev, D. Vickrey, and D. Koller. Learning a meta-level prior for feature relevance from multiple related tasks. In ICML, pages 489-496, 2007.        